We provide an in-depth example of modular ontology engineering with ontology design patterns. The style and content of this chapter is adapted from previous work and tutorials on Modular Ontology Modeling. It offers expanded steps and updated tool information. The tutorial is largely self-contained, but assumes that the reader is familiar with the Web Ontology Language OWL; however, we do briefly review some foundational concepts. By the end of the tutorial, we expect the reader to have an understanding of the underlying motivation and methodology for producing a modular ontology.
{"title":"Modular Ontology Modeling: A Tutorial","authors":"C. Shimizu, P. Hitzler, Adila Alfa Krisnadhi","doi":"10.3233/ssw200032","DOIUrl":"https://doi.org/10.3233/ssw200032","url":null,"abstract":"We provide an in-depth example of modular ontology engineering with ontology design patterns. The style and content of this chapter is adapted from previous work and tutorials on Modular Ontology Modeling. It offers expanded steps and updated tool information. The tutorial is largely self-contained, but assumes that the reader is familiar with the Web Ontology Language OWL; however, we do briefly review some foundational concepts. By the end of the tutorial, we expect the reader to have an understanding of the underlying motivation and methodology for producing a modular ontology.","PeriodicalId":102100,"journal":{"name":"Applications and Practices in Ontology Design, Extraction, and Reasoning","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114349182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The problem of querying RDF data is a central issue for the development of the Semantic Web. The query language SPARQL has become the standard language for querying RDF since its W3C standardization in 2008. However, the 2008 version of this language missed some important functionalities: reasoning capabilities to deal with RDFS and OWL vocabularies, navigational capabilities to exploit the graph structure of RDF data, and a general form of recursion much needed to express some natural queries. To overcome those limitations, a new version of SPARQL, called SPARQL 1.1, was released in 2013, which includes entailment regimes for RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. Nevertheless, there are useful navigation patterns that cannot be expressed in SPARQL 1.1, and the language lacks a general mechanism to express recursive queries. This chapter is a gentle introduction to a tractable rule-based query language, in fact, an extension of Datalog with value invention, stratified negation, and falsum, that is powerful enough to define SPARQL queries enhanced with the desired functionalities focussing on a core fragment of the OWL 2 QL profile of OWL 2.
{"title":"Querying the Semantic Web via Rules","authors":"M. Arenas, G. Gottlob, Andreas Pieris","doi":"10.3233/ssw200044","DOIUrl":"https://doi.org/10.3233/ssw200044","url":null,"abstract":"The problem of querying RDF data is a central issue for the development of the Semantic Web. The query language SPARQL has become the standard language for querying RDF since its W3C standardization in 2008. However, the 2008 version of this language missed some important functionalities: reasoning capabilities to deal with RDFS and OWL vocabularies, navigational capabilities to exploit the graph structure of RDF data, and a general form of recursion much needed to express some natural queries. To overcome those limitations, a new version of SPARQL, called SPARQL 1.1, was released in 2013, which includes entailment regimes for RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. Nevertheless, there are useful navigation patterns that cannot be expressed in SPARQL 1.1, and the language lacks a general mechanism to express recursive queries. This chapter is a gentle introduction to a tractable rule-based query language, in fact, an extension of Datalog with value invention, stratified negation, and falsum, that is powerful enough to define SPARQL queries enhanced with the desired functionalities focussing on a core fragment of the OWL 2 QL profile of OWL 2.","PeriodicalId":102100,"journal":{"name":"Applications and Practices in Ontology Design, Extraction, and Reasoning","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124839104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this chapter, an overview of the state of the art on knowledge graph generation is provided, with focus on the two prevalent mapping languages: the W3C recommended R2RML and its generalisation RML. We look into details on their differences and explain how knowledge graphs, in the form of RDF graphs, can be generated with each one of the two mapping languages. Then we assess if the vocabulary terms were properly applied to the data and no violations occurred on their use, either using R2RML or RML to generate the desired knowledge graph.
{"title":"High-Quality Knowledge Graphs Generation: R2RML and RML Comparison, Rules Validation and Inconsistency Resolution","authors":"Anastasia Dimou","doi":"10.3233/ssw200035","DOIUrl":"https://doi.org/10.3233/ssw200035","url":null,"abstract":"In this chapter, an overview of the state of the art on knowledge graph generation is provided, with focus on the two prevalent mapping languages: the W3C recommended R2RML and its generalisation RML. We look into details on their differences and explain how knowledge graphs, in the form of RDF graphs, can be generated with each one of the two mapping languages. Then we assess if the vocabulary terms were properly applied to the data and no violations occurred on their use, either using R2RML or RML to generate the desired knowledge graph.","PeriodicalId":102100,"journal":{"name":"Applications and Practices in Ontology Design, Extraction, and Reasoning","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115975834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Albert Meroño-Peñuela, V. D. Boer, M. Erp, R. Zijdeman, R. Mourits, W. Melder, A. Rijpma, Ruben Schalk
One of the most important goals of digital humanities is to provide researchers with data and tools for new research questions, either by increasing the scale of scholarly studies, linking existing databases, or improving the accessibility of data. Here, the FAIR principles provide a useful framework. Integrating data from diverse humanities domains is not trivial, research questions such as “was economic wealth equally distributed in the 18th century?”, or “what are narratives constructed around disruptive media events?”) and preparation phases (e.g. data collection, knowledge organisation, cleaning) of scholars need to be taken into account. In this chapter, we describe the ontologies and tools developed and integrated in the Dutch national project CLARIAH to address these issues across datasets from three fundamental domains or “pillars” of the humanities (linguistics, social and economic history, and media studies) that have paradigmatic data representations (textual corpora, structured data, and multimedia). We summarise the lessons learnt from using such ontologies and tools in these domains from a generalisation and reusability perspective.
{"title":"CLARIAH: Enabling Interoperability Between Humanities Disciplines with Ontologies","authors":"Albert Meroño-Peñuela, V. D. Boer, M. Erp, R. Zijdeman, R. Mourits, W. Melder, A. Rijpma, Ruben Schalk","doi":"10.3233/ssw200036","DOIUrl":"https://doi.org/10.3233/ssw200036","url":null,"abstract":"One of the most important goals of digital humanities is to provide researchers with data and tools for new research questions, either by increasing the scale of scholarly studies, linking existing databases, or improving the accessibility of data. Here, the FAIR principles provide a useful framework. Integrating data from diverse humanities domains is not trivial, research questions such as “was economic wealth equally distributed in the 18th century?”, or “what are narratives constructed around disruptive media events?”) and preparation phases (e.g. data collection, knowledge organisation, cleaning) of scholars need to be taken into account. In this chapter, we describe the ontologies and tools developed and integrated in the Dutch national project CLARIAH to address these issues across datasets from three fundamental domains or “pillars” of the humanities (linguistics, social and economic history, and media studies) that have paradigmatic data representations (textual corpora, structured data, and multimedia). We summarise the lessons learnt from using such ontologies and tools in these domains from a generalisation and reusability perspective.","PeriodicalId":102100,"journal":{"name":"Applications and Practices in Ontology Design, Extraction, and Reasoning","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128258164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In Digital Humanities, one of the main challenge consists in capturing the structure of complex information in data models and ontologies, in particular when connections between terms are not trivial. This is typically the case for librarian music data. In this chapter, we provide some good practices for representing complex knowledge using the DOREMUS ontology as an exemplary case. We also show various applications of a Knowledge Graph leveraging on the ontology, ranging from an exploratory search engine, a recommender system and a conversational agent enabling to answer classical music questions.
{"title":"Representing Complex Knowledge for Exploration and Recommendation: The Case of Classical Music Information","authors":"Pasquale Lisena, Raphael Troncy","doi":"10.3233/ssw200038","DOIUrl":"https://doi.org/10.3233/ssw200038","url":null,"abstract":"In Digital Humanities, one of the main challenge consists in capturing the structure of complex information in data models and ontologies, in particular when connections between terms are not trivial. This is typically the case for librarian music data. In this chapter, we provide some good practices for representing complex knowledge using the DOREMUS ontology as an exemplary case. We also show various applications of a Knowledge Graph leveraging on the ontology, ranging from an exploratory search engine, a recommender system and a conversational agent enabling to answer classical music questions.","PeriodicalId":102100,"journal":{"name":"Applications and Practices in Ontology Design, Extraction, and Reasoning","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giuseppe Cota, Riccardo Zese, Elena Bellodi, E. Lamma, Fabrizio Riguzzi
While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics. In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics. The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.
{"title":"A Framework for Reasoning on Probabilistic Description Logics","authors":"Giuseppe Cota, Riccardo Zese, Elena Bellodi, E. Lamma, Fabrizio Riguzzi","doi":"10.3233/SSW200040","DOIUrl":"https://doi.org/10.3233/SSW200040","url":null,"abstract":"While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics. In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics. The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.","PeriodicalId":102100,"journal":{"name":"Applications and Practices in Ontology Design, Extraction, and Reasoning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123005836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DL^N is a recent approach that extends description logics with defeasible reasoning capabilities. In this paper we provide an overview on DL^N, illustrating the underlying knowledge engineering requirements as well as the characteristic features that preserve DL^N from some recurrent semantic and computational drawbacks. We also compare DL^N with some alternative nonmonotonic semantics, enlightening the relationships between the KLM postulates and DL^N.
{"title":"Defeasible reasoning in Description Logics: an overview on DLN","authors":"P. Bonatti, I. Petrova, L. Sauro","doi":"10.3233/ssw200043","DOIUrl":"https://doi.org/10.3233/ssw200043","url":null,"abstract":"DL^N is a recent approach that extends description logics with defeasible reasoning capabilities. In this paper we provide an overview on DL^N, illustrating the underlying knowledge engineering requirements as well as the characteristic features that preserve DL^N from some recurrent semantic and computational drawbacks. We also compare DL^N with some alternative nonmonotonic semantics, enlightening the relationships between the KLM postulates and DL^N.","PeriodicalId":102100,"journal":{"name":"Applications and Practices in Ontology Design, Extraction, and Reasoning","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123400017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laura Giordano, Valentina Gliozzi, Antonio Lieto, Nicola Olivetto, G. Pozzato
In this work we describe preferential Description Logics of typicality, a nonmonotonic extension of standard Description Logics by means of a typicality operator T allowing to extend a knowledge base with inclusions of the form T(C) v D, whose intuitive meaning is that normally/typically Cs are also Ds. This extension is based on a minimal model semantics corresponding to a notion of rational closure, built upon preferential models. We recall the basic concepts underlying preferential Description Logics. We also present two extensions of the preferential semantics: on the one hand, we consider probabilistic extensions, based on a distributed semantics that is suitable for tackling the problem of commonsense concept combination, on the other hand, we consider other strengthening of the rational closure semantics and construction to avoid the so-called blocking of property inheritance problem.
在这项工作中，我们描述了典型的优先描述逻辑，这是标准描述逻辑的非单调扩展，通过典型算子T允许扩展包含形式为T(C) v D的知识库，其直观含义是通常/典型的C也是D。这个扩展是基于一个最小的模型语义，对应于理性闭包的概念，建立在优先模型之上。我们回顾优先描述逻辑的基本概念。我们还提出了优先语义的两种扩展:一方面，我们考虑了基于分布式语义的概率扩展，适合于解决常识性概念组合问题;另一方面，我们考虑了对合理闭包语义和构造的其他强化，以避免所谓的属性继承阻塞问题。
{"title":"Reasoning about Typicality and Probabilities in Preferential Description Logics","authors":"Laura Giordano, Valentina Gliozzi, Antonio Lieto, Nicola Olivetto, G. Pozzato","doi":"10.3233/ssw200041","DOIUrl":"https://doi.org/10.3233/ssw200041","url":null,"abstract":"In this work we describe preferential Description Logics of typicality, a nonmonotonic extension of standard Description Logics by means of a typicality operator T allowing to extend a knowledge base with inclusions of the form T(C) v D, whose intuitive meaning is that normally/typically Cs are also Ds. This extension is based on a minimal model semantics corresponding to a notion of rational closure, built upon preferential models. We recall the basic concepts underlying preferential Description Logics. We also present two extensions of the preferential semantics: on the one hand, we consider probabilistic extensions, based on a distributed semantics that is suitable for tackling the problem of commonsense concept combination, on the other hand, we consider other strengthening of the rational closure semantics and construction to avoid the so-called blocking of property inheritance problem.","PeriodicalId":102100,"journal":{"name":"Applications and Practices in Ontology Design, Extraction, and Reasoning","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129321040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reasoning over OWL 2 is a very expensive task in general, and therefore the W3C identified tractable profiles exhibiting good computational properties. Ontological reasoning for many fragments of OWL 2 can be reduced to the evaluation of Datalog queries. This paper surveys some of these compilations, and in particular the one addressing queries over Horn-$mathcal{SHIQ}$ knowledge bases and its implementation in DLV2 enanched by a new version of the Magic Sets algorithm.
{"title":"Large-scale Ontological Reasoning via Datalog","authors":"Mario Alviano, M. Manna","doi":"10.3233/ssw200045","DOIUrl":"https://doi.org/10.3233/ssw200045","url":null,"abstract":"Reasoning over OWL 2 is a very expensive task in general, and therefore the W3C identified tractable profiles exhibiting good computational properties. Ontological reasoning for many fragments of OWL 2 can be reduced to the evaluation of Datalog queries. This paper surveys some of these compilations, and in particular the one addressing queries over Horn-$mathcal{SHIQ}$ knowledge bases and its implementation in DLV2 enanched by a new version of the Magic Sets algorithm.","PeriodicalId":102100,"journal":{"name":"Applications and Practices in Ontology Design, Extraction, and Reasoning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128296791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Applications and Practices in Ontology Design, Extraction, and Reasoning","authors":"","doi":"10.3233/ssw49","DOIUrl":"https://doi.org/10.3233/ssw49","url":null,"abstract":"","PeriodicalId":102100,"journal":{"name":"Applications and Practices in Ontology Design, Extraction, and Reasoning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115210129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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